RAG Is Search, Not Memory — And the Difference Breaks AI Agents

Retrieval-Augmented Generation (RAG) works by finding documents that appear relevant to a query, but it cannot determine which of two conflicting documents reflects the current truth. Unlike genuine memory, RAG has no mechanism to track when one fact replaced another, so it passes contradictions directly to the model without resolution. This means a RAG-based system holds no actual beliefs and cannot be corrected, since it simply surfaces whatever exists in the index with equal confidence. The distinction matters most in real-world deployments where data changes over time, such as updated contracts or revised pricing tiers. AI startup AlphaNimble is documenting these limitations publicly as part of its work on Memuron, a dedicated memory system designed for AI agents.